Proposes a five-stage agentic AI framework for code review with human quality gates to maintain context, accountability, and team understanding.
The emerged security and privacy of llm agent: A survey with case studies
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The paper introduces a multi-agent LLM framework using ReAct-style agents with self-reflection for classifying telecom queries, anonymizing PII via k-anonymity and differential privacy, and translating expert responses for end users.
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Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review
Proposes a five-stage agentic AI framework for code review with human quality gates to maintain context, accountability, and team understanding.